Laplace-domain analysis of fluid line networks with applications to time-domain simulation and system parameter identification.

Networks of closed conduits containing pressurised fluid flow occur in many different instances throughout the natural and man made world. The dynamics of such networks are dependent not only on the complex interactions between the fluid body and the conduit material within each fluid line, but also on the coupling between different lines as they influence each other through their common junctions. The forward modelling (time-domain simulation), and inverse modelling (system parameter identification) of such systems is of great interest to many different research fields. An alternative approach to time-domain descriptions of fluid line networks is the Laplace-domain representation of these systems. A long standing limitation of these methods is that the frameworks for constructing Laplace-domain models have not been suitable for pipeline networks of an arbitrary topology. The objective of this thesis is to fundamentally extend the existing theory for Laplace-domain descriptions of hydraulic networks and explore the applications of this theory to forward and inverse modelling. The extensions are undertaken by the use of graph theory concepts to construct network admittance matrices based on the Laplace-domain solutions of the fundamental pipeline dynamics. This framework is extended to incorporate a very broad class of hydraulic elements. Through the use of the numerical inverse Laplace transform, the proposed theory forms the basis for an accurate and computationally efficient hydraulic network time-domain simulation methodology. The compact analytic nature of the network admittance matrix representation facilitates the development of two successful and statistically based parameter identification methodologies, one based on an oblique filtering approach combined with maximum likelihood estimation, and the other based on the expectation-maximisation algorithm.Thesis (Ph.D.) -- University of Adelaide, School of Civil, Environmental and Mining Engineering, 201

On the development of intelligent medical systems for pre-operative anaesthesia assessment

This thesis describes the research and development of a decision support tool for determining a medical patient's suitability for surgical anaesthesia. At present, there is a change in the way that patients are clinically assessedp rior to surgery. The pre-operative assessment, usually conducted by a qualified anaesthetist, is being more frequently performed by nursing grade staff. The pre-operative assessmenet xists to minimise the risk of surgical complications for the patient. Nursing grade staff are often not as experienced as qualified anaesthetists, and thus are not as well suited to the role of performing the pre-operative assessment. This research project used data collected during pre-operative assessments to develop a decision support tool that would assist the nurse (or anaesthetist) in determining whether a patient is suitable for surgical anaesthesia. The three main objectives are: firstly, to research and develop an automated intelligent systems technique for classifying heart and lung sounds and hence identifying cardio-respiratory pathology. Secondly, to research and develop an automated intelligent systems technique for assessing the patient's blood oxygen level and pulse waveform. Finally, to develop a decision support tool that would combine the assessmentsa bove in forming a decision as to whether the patient is suitable for surgical anaesthesia. Clinical data were collected from hospital outpatient departments and recorded alongside the diagnoses made by a qualified anaesthetist. Heart and lung sounds were collected using an electronic stethoscope. Using this data two ensembles of artificial neural networks were trained to classify the different heart and lung sounds into different pathology groups. Classification accuracies up to 99.77% for the heart sounds, and 100% for the lung sounds has been obtained. Oxygen saturation and pulse waveform measurements were recorded using a pulse oximeter. Using this data an artificial neural network was trained to discriminate between normal and abnormal pulse waveforms. A discrimination accuracy of 98% has been obtained from the system. A fuzzy inference system was generated to classify the patient's blood oxygen level as being either an inhibiting or non-inhibiting factor in their suitability for surgical anaesthesia. When tested the system successfully classified 100% of the test dataset. A decision support tool, applying the genetic programming evolutionary technique to a fuzzy classification system was created. The decision support tool combined the results from the heart sound, lung sound and pulse oximetry classifiers in determining whether a patient was suitable for surgical anaesthesia. The evolved fuzzy system attained a classification accuracy of 91.79%. The principal conclusion from this thesis is that intelligent systems, such as artificial neural networks, genetic programming, and fuzzy inference systems, can be successfully applied to the creation of medical decision support tools.EThOS - Electronic Theses Online ServiceMedicdirect.co.uk Ltd.GBUnited Kingdo

Supplementing Frequency Domain Interpolation Methods for Character Animation

The animation of human characters entails difficulties exceeding those met simulating objects, machines or plants. A person's gait is a product of nature affected by mood and physical condition. Small deviations from natural movement are perceived with ease by an unforgiving audience. Motion capture technology is frequently employed to record human movement. Subsequent playback on a skeleton underlying the character being animated conveys many of the subtleties of the original motion. Played-back recordings are of limited value, however, when integration in a virtual environment requires movements beyond those in the motion library, creating a need for the synthesis of new motion from pre-recorded sequences. An existing approach involves interpolation between motions in the frequency domain, with a blending space defined by a triangle network whose vertices represent input motions. It is this branch of character animation which is supplemented by the methods presented in this thesis, with work undertaken in three distinct areas. The first is a streamlined approach to previous work. It provides benefits including an efficiency gain in certain contexts, and a very different perspective on triangle network construction in which they become adjustable and intuitive user-interface devices with an increased flexibility allowing a greater range of motions to be blended than was possible with previous networks. Interpolation-based synthesis can never exhibit the same motion variety as can animation methods based on the playback of rearranged frame sequences. Limitations such as this were addressed by the second phase of work, with the creation of hybrid networks. These novel structures use properties of frequency domain triangle blending networks to seamlessly integrate playback-based animation within them. The third area focussed on was distortion found in both frequency- and time-domain blending. A new technique, single-source harmonic switching, was devised which greatly reduces it, and adds to the benefits of blending in the frequency domain

Generalising the simultaneous computation of the DFTs of two real sequences using a single N-point DFT

A general approach to the problem of simultaneous computation of the discrete Fourier transform (DFT) of two sequences of length N, which may be real, imaginary, conjugated symmetric or conjugated anti-symmetric, using a single N-point DFT of a complex sequence is presented. The framework developed is applied to the simultaneous computation of two DFTs, one DFT and one inverse DFT (IDFT) or two IDFTs for real N-point sequences.http://www.sciencedirect.com/science/article/B6V18-452WDYB-1/1/8f15d68b3223a57423b4a429383493d

Digital Signal Processing (Second Edition)

This book provides an account of the mathematical background, computational methods and software engineering associated with digital signal processing. The aim has been to provide the reader with the mathematical methods required for signal analysis which are then used to develop models and algorithms for processing digital signals and finally to encourage the reader to design software solutions for Digital Signal Processing (DSP). In this way, the reader is invited to develop a small DSP library that can then be expanded further with a focus on his/her research interests and applications. There are of course many excellent books and software systems available on this subject area. However, in many of these publications, the relationship between the mathematical methods associated with signal analysis and the software available for processing data is not always clear. Either the publications concentrate on mathematical aspects that are not focused on practical programming solutions or elaborate on the software development of solutions in terms of working ‘black-boxes’ without covering the mathematical background and analysis associated with the design of these software solutions. Thus, this book has been written with the aim of giving the reader a technical overview of the mathematics and software associated with the ‘art’ of developing numerical algorithms and designing software solutions for DSP, all of which is built on firm mathematical foundations. For this reason, the work is, by necessity, rather lengthy and covers a wide range of subjects compounded in four principal parts. Part I provides the mathematical background for the analysis of signals, Part II considers the computational techniques (principally those associated with linear algebra and the linear eigenvalue problem) required for array processing and associated analysis (error analysis for example). Part III introduces the reader to the essential elements of software engineering using the C programming language, tailored to those features that are used for developing C functions or modules for building a DSP library. The material associated with parts I, II and III is then used to build up a DSP system by defining a number of ‘problems’ and then addressing the solutions in terms of presenting an appropriate mathematical model, undertaking the necessary analysis, developing an appropriate algorithm and then coding the solution in C. This material forms the basis for part IV of this work. In most chapters, a series of tutorial problems is given for the reader to attempt with answers provided in Appendix A. These problems include theoretical, computational and programming exercises. Part II of this work is relatively long and arguably contains too much material on the computational methods for linear algebra. However, this material and the complementary material on vector and matrix norms forms the computational basis for many methods of digital signal processing. Moreover, this important and widely researched subject area forms the foundations, not only of digital signal processing and control engineering for example, but also of numerical analysis in general. The material presented in this book is based on the lecture notes and supplementary material developed by the author for an advanced Masters course ‘Digital Signal Processing’ which was first established at Cranfield University, Bedford in 1990 and modified when the author moved to De Montfort University, Leicester in 1994. The programmes are still operating at these universities and the material has been used by some 700++ graduates since its establishment and development in the early 1990s. The material was enhanced and developed further when the author moved to the Department of Electronic and Electrical Engineering at Loughborough University in 2003 and now forms part of the Department’s post-graduate programmes in Communication Systems Engineering. The original Masters programme included a taught component covering a period of six months based on two semesters, each Semester being composed of four modules. The material in this work covers the first Semester and its four parts reflect the four modules delivered. The material delivered in the second Semester is published as a companion volume to this work entitled Digital Image Processing, Horwood Publishing, 2005 which covers the mathematical modelling of imaging systems and the techniques that have been developed to process and analyse the data such systems provide. Since the publication of the first edition of this work in 2003, a number of minor changes and some additions have been made. The material on programming and software engineering in Chapters 11 and 12 has been extended. This includes some additions and further solved and supplementary questions which are included throughout the text. Nevertheless, it is worth pointing out, that while every effort has been made by the author and publisher to provide a work that is error free, it is inevitable that typing errors and various ‘bugs’ will occur. If so, and in particular, if the reader starts to suffer from a lack of comprehension over certain aspects of the material (due to errors or otherwise) then he/she should not assume that there is something wrong with themselves, but with the author

Statistical models for natural sounds

It is important to understand the rich structure of natural sounds in order to solve important tasks, like automatic speech recognition, and to understand auditory processing in the brain. This thesis takes a step in this direction by characterising the statistics of simple natural sounds. We focus on the statistics because perception often appears to depend on them, rather than on the raw waveform. For example the perception of auditory textures, like running water, wind, fire and rain, depends on summary-statistics, like the rate of falling rain droplets, rather than on the exact details of the physical source. In order to analyse the statistics of sounds accurately it is necessary to improve a number of traditional signal processing methods, including those for amplitude demodulation, time-frequency analysis, and sub-band demodulation. These estimation tasks are ill-posed and therefore it is natural to treat them as Bayesian inference problems. The new probabilistic versions of these methods have several advantages. For example, they perform more accurately on natural signals and are more robust to noise, they can also fill-in missing sections of data, and provide error-bars. Furthermore, free-parameters can be learned from the signal. Using these new algorithms we demonstrate that the energy, sparsity, modulation depth and modulation time-scale in each sub-band of a signal are critical statistics, together with the dependencies between the sub-band modulators. In order to validate this claim, a model containing co-modulated coloured noise carriers is shown to be capable of generating a range of realistic sounding auditory textures. Finally, we explored the connection between the statistics of natural sounds and perception. We demonstrate that inference in the model for auditory textures qualitatively replicates the primitive grouping rules that listeners use to understand simple acoustic scenes. This suggests that the auditory system is optimised for the statistics of natural sounds

Techniques améliorées pour la cryptanalyse des primitives symétriques

This thesis proposes improvements which can be applied to several techniques for the cryptanalysis of symmetric primitives. Special attention is given to linear cryptanalysis, for which a technique based on the fast Walsh transform was already known (Collard et al., ICISIC 2007). We introduce a generalised version of this attack, which allows us to apply it on key recovery attacks over multiple rounds, as well as to reduce the complexity of the problem using information extracted, for example, from the key schedule. We also propose a general technique for speeding key recovery attacks up which is based on the representation of Sboxes as binary decision trees. Finally, we showcase the construction of a linear approximation of the full version of the Gimli permutation using mixed-integer linear programming (MILP) optimisation.Dans cette thèse, on propose des améliorations qui peuvent être appliquées à plusieurs techniques de cryptanalyse de primitives symétriques. On dédie une attention spéciale à la cryptanalyse linéaire, pour laquelle une technique basée sur la transformée de Walsh rapide était déjà connue (Collard et al., ICISC 2007). On introduit une version généralisée de cette attaque, qui permet de l'appliquer pour la récupération de clé considerant plusieurs tours, ainsi que le réduction de la complexité du problème en utilisant par example des informations provénantes du key-schedule. On propose aussi une technique générale pour accélérer les attaques par récupération de clé qui est basée sur la représentation des boîtes S en tant que arbres binaires. Finalement, on montre comment on a obtenu une approximation linéaire sur la version complète de la permutation Gimli en utilisant l'optimisation par mixed-integer linear programming (MILP)

On robust statistical outlier analysis for damage identification

This thesis aims to contribute towards the development of reliable and accurate damage detection monitoring frameworks, applicable for a range of structural health and condition monitoring problems. Central to this purpose, is to be able to detect damage patterns embedded in a system's vibration signal responses sufficiently early. This will enable a condition-based maintenance and inspection to be carried out so as to prevent potentially catastrophic events, as related to each application domain. Firstly, to obviate reliance on data labels, an inclusive outlier analysis study is conducted by means of robust multivariate statistical analysis and a range of other (more common) outlier detection techniques, in both multivariate and time-series settings. Given the parametric nature of robust multivariate statistical techniques, it has also been possible to characterise outliers according to their influence on a method's estimates. Secondly, novelty detection is explored, in which a set of samples representing the nominal state of the system, is assumed to be available. This set includes observations from a system with its dynamics being significantly influenced by environmental and operational variability. Finally, this thesis explored the potential of utilising certain robust techniques as a pre-processing step on damage sensitive features (contaminated with outliers) for novelty detection tasks. Given the large volume of observations, both experimental and computational, different damage sensitive features were extracted, some of which were specific to the range of problems / types of damage being investigated. The performance, in terms of both sensitivity in damage detection and immunity to environmental and operational variability, was assessed for each damage sensitive feature, in combination to the outlier and novelty detection technique used. This thesis has introduced to the condition and structural health monitoring fields a range of methods from robust statistics with attractive properties, such as the effective unmasking of outliers

Wideband and Relativistic Superradiance in Astrophysics

In the quantum phenomenon of superradiance (SR) a population of inverted particles evolves, through its interaction with the quantized vacuum radiation field, into a highly entangled state capable of generating much greater radiative emission than predicted by the independent spontaneous decay of its constituent particles. The phenomenon has recently been applied to transient astrophysical processes but has thus far been restricted to particles sharing a common velocity. This thesis researches the effects of astrophysical velocity distributions upon SR, which are distinct from conventional regimes of the quantum optics literature in that they may possess extremely wide bandwidths, turbulent statistical properties, or highly relativistic mean velocities. An important result of this thesis is the derivation of two novel algorithms for simulating widely Doppler broadened SR, each offering improved numerical complexity scaling over conventional methods. In the first, a Fourier domain representation is derived for the velocity dependent partial differential equations describing a population inversion interacting with the radiation field; this representation generalises an existing quasi-steady state maser model to the transient SR regime. In the second, the electric field is represented by a collection of fields, each representing photon creation or annihilation on resonance with a particular velocity channel; the symmetry of this representation leads to a numerically advantageous algorithm for many velocity broadened systems. I apply this latter algorithm to investigate the effects of pumping mechanisms and velocity distribution statistics upon transient SR processes in widely broadened astrophysical media. I demonstrate that the orientation of the pumping mechanism as well as turbulent properties of the velocity distribution critically affect transient SR structure in a widely Doppler broadened sample. The final project of this thesis develops a relativistic model of SR built upon canonical quantization of a covariant Lagrangian for the matter-radiation interaction. I apply the diagrammatic method alongside numerical techniques to compute the particle state reduced density operator\u27s time evolution from the relativistic two-particle SR Hamiltonian, and make quantitative conclusions regarding the effect of relativistic velocity coherence upon SR intensity measurements in the observer\u27s frame